Embodiments of the present technique relate generally to health monitoring, and more particularly to methods and systems for fall detection.
Unintentional falls are one of the most complex and costly health issues facing elderly people. Recent studies show that approximately one in every three adults age 65 years or older falls each year, and about 30 percent of these falls result in serious injuries. Particularly, people who experience a fall event at home may remain on the ground for an extended period of time as help may not be immediately available. The studies indicate a high mortality rate amongst such people who remain on the ground for an hour or more after a fall.
Fall detection (FD), therefore, has become a major focus of healthcare facilities. Conventionally, healthcare facilities employ nursing staff to monitor a person around the clock. In settings such as assisted living or independent community life, however, the desire for privacy and the associated expense render such constant monitoring undesirable. Accordingly, FD systems based on wearable devices including sensors such as accelerometers, gyroscopes and/or microphones have been proposed. These devices, however, may need to be activated by a fallen person using a push-button to alert appropriate personnel or a health monitoring system. FD systems based on such wearable devices, therefore, may be successful only if the person wears the sensing devices at all times and is physically and cognitively able to activate the alarm when an emergency arises.
Therefore, in recent times, video-based FD systems are being widely investigated for efficient fall detection. Conventional video-based FD systems process images of the person's motion in real time to evaluate if detected horizontal and vertical velocities corresponding to the person's motion indicate a fall event. Determination of the horizontal and vertical velocities while detecting human falls involves use of complex computations and classification algorithms, thereby requiring a great deal of processing power and expensive equipment. Additionally, such video-based FD systems fail to robustly detect slow falls that may be characterized by low horizontal and vertical velocities. Further, use of such video-based FD systems typically involves acquisition of personally identifiable information leading to numerous privacy concerns. Specifically, constant monitoring and acquisition of identifiable videos is considered by many people to be an intrusion of their privacy.
It may therefore be desirable to develop an effective method and system for detecting high-risk movements, especially human fall events. Specifically, there is a need for a relatively inexpensive FD system capable of easily and accurately computing one or more parameters indicative of potential fall events such as a size and a distance corresponding to objects disposed in an FD environment. Additionally, there is a need for an FD method and a system that non-intrusively yet reliably detect a wide variety of falls with a fairly low instance of false alarms.